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葉丙成
這是一個機率的入門課程，著重的是教授機率基本概念。課程內容和作業都使用生活化的例子，希望讓同學們快樂學習、快速培養同學們對於機率的洞察力與應用能力。
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Syllabus
WEEK 1
歡迎來到「頑想學概率：機率一」第一週課程！本週主題有三個： 1. 機率的概論──機率的本質是什麼？ 2. 所有機率課本都會講到的：集合論 3. 機率學中一些重要專有名詞含義的介紹
WEEK 2
本週的兩個主題：1. 神聖的機率三公理和衍生的性質 2. 機率學中不能不知道的「條件機率」概念 很有趣哦！
Read more
Syllabus
WEEK 1
歡迎來到「頑想學概率：機率一」第一週課程！本週主題有三個： 1. 機率的概論──機率的本質是什麼？ 2. 所有機率課本都會講到的：集合論 3. 機率學中一些重要專有名詞含義的介紹
WEEK 2
本週的兩個主題：1. 神聖的機率三公理和衍生的性質 2. 機率學中不能不知道的「條件機率」概念 很有趣哦！
Read more
WEEK 3
本週的三個主題：1. 不同事件，機率的獨立性 2. 使用圖解的方式計算複雜難算的機率 3. 我們可以怎麼樣利用數數的方式，來幫我們數東西、算機率，這跟算機率有什麼關係？
WEEK 4 (1)
本週有四個主題：1. 隨機變數的概念 2. 累積分布函數 (CDF) 3. 機率質量函數 (PMF) 4. 常見的離散機率分佈
WEEK 4 (2)
「頑想學概率：機率一」將在「離散機率分佈」告一段落。 其他更多好玩有趣的機率課程，將會在「頑想學概率：機率二」課程做介紹，我們下次見！
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Uses real-world examples to make learning enjoyable and relatable
Suitable for beginners who want to build a strong foundation in probability
Introduces fundamental probability concepts, including the axioms of probability and conditional probability
Emphasizes developing an intuitive understanding of probability rather than focusing solely on mathematical formulas
May require additional resources and practice for those seeking a more in-depth understanding of probability
Excludes topics related to probability distributions, which may be important for some applications
Save this course
Save 頑想學概率：機率一 (Probability (1)) to your list so you can find it easily later:
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Reviews summary
趣味概率入門
本課程以輕鬆活潑的例子講解概率基本概念，適合對數理有興趣的高中生學習。課程題目靈活有趣，難度適中，能激起學習意願，培養獨立思考和學習能力。老師上課幽默風趣，能讓學生在快樂的氛圍中學習概率。
適合對數理有興趣的高中生學習。
"適合對數理有興趣的高中生~"
課程培養獨立思考與學習能力。
"很棒的课程，培养了我独立思考独立学习的能力。"
課程題目靈活有趣，難度適中。
"例子跟課程題目很有趣，能激起學習意願練習的題目有一定難度，可以多想一下"
"Very interesting and challenging questions."
課程講解不生硬，例子與題目趣味橫生。
"老師把課程講解的不生硬，習題也很靈活，非常推薦"
老師上課幽默風趣，教學方式生動活潑。
"老師上得很好 但無法重置截止日期"
"課程很棒，老師的教學方式幽默有趣"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in 頑想學概率：機率一 (Probability (1)) with these
activities:
Review probability theory
Show steps
Review the basics of probability theory to ensure a solid foundation for this course.
Browse courses on
Probability Theory
Show steps
-
Read through probability handouts and notes.
-
Review probability concepts through videos.
-
Solve practice problems to test understanding.
Probability problem-solving exercises
Show steps
Strengthen your problem-solving abilities by working through a variety of probability exercises.
Show steps
-
Attempt a set of 10 practice problems on conditional probability.
-
Attempt a set of 10 practice problems on Bayes' theorem.
-
Attempt a set of 10 practice problems on counting techniques.
Create an infographic on conditional probability
Show steps
Reinforce your understanding of conditional probability by creating a visual representation of the concept.
Show steps
-
Identify the key concepts of conditional probability.
-
Design a visually appealing infographic that explains these concepts clearly.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Review the book 'Probability for Data Scientists' by Jake VanderPlas
Show steps
Expand your understanding of probability by reading and engaging with a comprehensive book on the subject.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Read selected chapters of the book to broaden knowledge on probability concepts
-
Solve exercises and problems presented in the book to test understanding
Volunteer at a local math tutoring center
Show steps
Apply your probability knowledge and gain practical experience by assisting students with their probability-related questions.
Show steps
-
Contact local math tutoring centers to inquire about volunteer opportunities focused on probability.
-
Prepare a lesson plan on a specific probability topic to introduce to students.
-
Attend the volunteer sessions and assist students with their probability questions.
Build a probability calculator
Show steps
Reinforce your understanding of probability by developing a tool that performs probability calculations.
Show steps
-
Choose a programming language and development environment.
-
Design the interface and functionality of the calculator.
-
Implement the probability calculations using appropriate algorithms.
-
Test the calculator thoroughly to ensure accuracy and reliability.
Develop a presentation on the applications of probability in machine learning
Show steps
Synthesize your knowledge of probability and machine learning by creating a presentation that showcases the practical applications of probability in this field.
Show steps
-
Research and gather information on the applications of probability in machine learning.
-
Design a visually appealing and informative presentation.
-
Practice delivering the presentation to ensure clarity and effectiveness.
Review probability theory
Show steps
Review the basics of probability theory to ensure a solid foundation for this course.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Read through probability handouts and notes.
- Review probability concepts through videos.
- Solve practice problems to test understanding.
Probability problem-solving exercises
Show steps
Strengthen your problem-solving abilities by working through a variety of probability exercises.
Show steps
Show steps
Show steps
- Attempt a set of 10 practice problems on conditional probability.
- Attempt a set of 10 practice problems on Bayes' theorem.
- Attempt a set of 10 practice problems on counting techniques.
Create an infographic on conditional probability
Show steps
Reinforce your understanding of conditional probability by creating a visual representation of the concept.
Show steps
Show steps
Show steps
- Identify the key concepts of conditional probability.
- Design a visually appealing infographic that explains these concepts clearly.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Review the book 'Probability for Data Scientists' by Jake VanderPlas
Show steps
Expand your understanding of probability by reading and engaging with a comprehensive book on the subject.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters of the book to broaden knowledge on probability concepts
- Solve exercises and problems presented in the book to test understanding
Volunteer at a local math tutoring center
Show steps
Apply your probability knowledge and gain practical experience by assisting students with their probability-related questions.
Show steps
Show steps
Show steps
- Contact local math tutoring centers to inquire about volunteer opportunities focused on probability.
- Prepare a lesson plan on a specific probability topic to introduce to students.
- Attend the volunteer sessions and assist students with their probability questions.
Build a probability calculator
Show steps
Reinforce your understanding of probability by developing a tool that performs probability calculations.
Show steps
Show steps
Show steps
- Choose a programming language and development environment.
- Design the interface and functionality of the calculator.
- Implement the probability calculations using appropriate algorithms.
- Test the calculator thoroughly to ensure accuracy and reliability.
Develop a presentation on the applications of probability in machine learning
Show steps
Synthesize your knowledge of probability and machine learning by creating a presentation that showcases the practical applications of probability in this field.
Show steps
Show steps
Show steps
- Research and gather information on the applications of probability in machine learning.
- Design a visually appealing and informative presentation.
- Practice delivering the presentation to ensure clarity and effectiveness.
Career center
Learners who complete 頑想學概率：機率一 (Probability (1)) will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians may use probability to develop statistical models for making predictions and decisions. Learning basic probability concepts will provide a foundation for building upon in future statistician roles. Especially for statisticians that work on data analysis, quality control, and market research.
Academic Researcher
Academic Researcher
Academic Researchers may inspire new discoveries in probability theory within this course. Learning basic foundational probability concepts will provide a vital foundation for building upon in future research endeavors. Statistics and Mathematics graduates may have the statistical background needed to succeed in this role.
Data Analyst
Data Analyst
Data Analysts may use probability to develop statistical models needed for discovering patterns, trends, and making predictions from data. Learning basic probability concepts will provide a foundation for building upon in future data analysis roles. Especially for analysts that work with risk prediction, modeling, and forecasting.
Financial Analyst
Financial Analyst
Financial Analysts may use probability to develop financial models for making investment decisions. Learning basic probability concepts will provide a foundation for building upon in future financial analysis roles. Especially for analysts that work with portfolio management, risk assessment, and financial planning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers may use probability to develop probabilistic models for making predictions and decisions. Learning basic probability concepts will provide a foundation for building upon in future machine learning engineering roles. Especially for engineers that work on fraud detection, spam filtering, and language translation.
Quantitative Risk Analyst
Quantitative Risk Analyst
Quantitative Risk Analysts may use probability to develop models for assessing and managing risk. Learning basic probability concepts will provide a foundation for building upon in future quantitative risk analysis roles. Especially for analysts that work with risk prediction, modeling, and portfolio optimization.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts may use probability to develop models for optimizing operations and decision-making. Learning basic probability concepts will provide a foundation for building upon in future operations research roles. Especially for analysts that work on supply chain management, inventory control, and project management.
Actuary
Actuary
Actuaries may use probability to develop models for assessing and managing risk. Learning basic probability concepts will provide a foundation for building upon in future actuary roles. Especially for actuaries that work with insurance, pensions, and employee benefits.
Risk Manager
Risk Manager
Risk Managers may use probability to develop models for assessing and managing risk. Learning basic probability concepts will provide a foundation for building upon in future risk management roles. Especially for managers that work with risk prediction, regulatory compliance, and insurance.
Underwriter
Underwriter
Underwriters may use probability to develop models for assessing and managing risk. Learning basic probability concepts will provide a foundation for building upon in future underwriting roles. Especially for underwriters that work with insurance, banking, and lending.
Financial Quantitative Analyst
Financial Quantitative Analyst
Financial Quantitative Analysts may use probability to develop mathematical models for predicting future financial trends. Learning basic probability concepts will provide a foundation for building upon in future financial analysis roles. Especially for quants that work with risk prediction, pricing, and portfolio optimization.
Data Scientist
Data Scientist
Data Scientists may use probability to develop statistical models for making predictions and decisions. Learning basic probability concepts will provide a foundation for building upon in future data science roles. Especially for data scientists that work on data analysis, machine learning, and artificial intelligence.
Trader
Trader
Traders may use probability to develop models for predicting future prices. Learning basic probability concepts will provide a foundation for building upon in future trading roles. Especially for traders that work with stocks, bonds, and derivatives.
Software Engineer
Software Engineer
Software Engineers may use probability to develop software that makes predictions and decisions. Learning basic probability concepts will provide a foundation for building upon in future software engineering roles. Especially for engineers that work on developing AI, machine learning, and data mining software.
Teacher
Teacher
Teachers may use probability to teach students about math and statistics. Learning basic probability concepts will provide a foundation for building upon in future teaching roles. Especially for teachers that work with high school or college students.
For more career information including salaries, visit:
OpenCourser.com/course/5rri7g/Wan
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
頑想學概率：機率一 (Probability (1)).
統計推論的經典教科書，涵蓋點估計、區間估計、假設檢定等主題，適合進階學習者。
以現代觀點介紹機率論和統計學，涵蓋廣泛主題，包括貝氏推論、大型偏差和隨機過程。
Save
從貝氏觀點探討機率論，強調主觀機率和訊息論的應用。
針對初學者編寫，以淺顯易懂的方式介紹機率論的基本概念和應用。
透過直觀入門機率理論的數學基礎，提供讀者建立必要的直觀和技能。
從機率論的角度介紹機器學習，適合對機率論和機器學習都有興趣的讀者。
嚴謹而全面地探討機率論的數學基礎，適合有微積分和線性代數基礎的讀者。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5rri7g/Wan
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Level
Introductory
Via
Coursera
Institution
National Taiwan University
Instructor
葉丙成
Language
中文
Transcript
中文
Good to know
Uses real-world examples to make learning enjoyable and relatable
Suitable for beginners who want to build a strong foundation in probability
Introduces fundamental probability concepts, including the axioms of probability and conditional probability
Emphasizes developing an intuitive understanding of probability rather than focusing solely on mathematical formulas
May require additional resources and practice for those seeking a more in-depth understanding of probability
Excludes topics related to probability distributions, which may be important for some applications
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